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arXiv · 2006.09261

Structured and Localized Image Restoration

Thomas Eboli·Alex Nowak-Vila·Jian Sun·Francis Bach·Jean Ponce·Alessandro Rudi
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Abstract

We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning. We optimize a penalized energy function regularized by a sum of terms measuring the distance between patches to be restored and clean patches from an external database gathered beforehand. The resulting estimator comes with strong statistical guarantees leveraging local dependency properties of overlapping patches. We derive the corresponding algorithms for energies based on the mean-squared and Euclidean norm errors. Finally, we demonstrate the practical effectiveness of our model on different image restoration problems using standard benchmarks.

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Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis Bach, Jean Ponce, Alessandro Rudi. 2020-06-16. Structured and Localized Image Restoration. https://arxiv.org/abs/2006.09261

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